Medical Image Retrieval Based on Simplified Multi-Wavelet Transform and Shape Feature

2014 ◽  
Vol 513-517 ◽  
pp. 2871-2875
Author(s):  
Xin Rui Wang ◽  
Yun Feng Yang

A novel medical image retrieval method based on Simplified Multi-wavelet Transform and Shape Feature was proposed in the paper, which included coarse and fine retrieval procedure. In the procedure of the coarse retrieval, Canny operator was used to extract edges of images. Moreover, contour lines were obtained by using the method of scan lines. At last, the coarse retrieval results of the images can be accomplished by using contour lines of images. In the procedure of the fine retrieval, the simplified multi-wavelet transform was used to decompose images at first, then, only the high frequency coefficients in the vertical directions were selected as retrieval objects. And hierarchical retrieval strategy was selected to accomplish the fine retrieval. This method not only can reduce the computational complexity effectively, but also can make full use of high frequency information of original images. Experiments showed that the accuracy of the retrieved results can be ensured.

2012 ◽  
Author(s):  
Yixiao Zhou ◽  
Yan Huang ◽  
Haibin Ling ◽  
Jingliang Peng

2017 ◽  
Vol 59 ◽  
pp. 131-139 ◽  
Author(s):  
Wenbo Li ◽  
Haiwei Pan ◽  
Pengyuan Li ◽  
Xiaoqin Xie ◽  
Zhiqiang Zhang

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval


2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Sun Xiaoming ◽  
Zhang Ning ◽  
Wu Haibin ◽  
Yu Xiaoyang ◽  
Wu Xue ◽  
...  

Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.


Sign in / Sign up

Export Citation Format

Share Document